library(readr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(ggplot2)
library(gapminder)
library(glue)marriage_df <- read_csv(file = glue("/lnet/aic/personal/cinkova/",
"R_BEGINNERS_SHORT/datasets_ATRIUM/gapminder_firstmarriage.csv"))Rows: 185 Columns: 2
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): country
dbl (1): 2005
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# or copy it to your location and read from there
str(marriage_df)spc_tbl_ [185 × 2] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ country: chr [1:185] "Afghanistan" "Albania" "Algeria" "Angola" ...
$ 2005 : num [1:185] 17.8 23.3 29.6 NA 23.3 ...
- attr(*, "spec")=
.. cols(
.. country = col_character(),
.. `2005` = col_double()
.. )
- attr(*, "problems")=<externalptr> Read the file as above and rename the column 2005 to “marriage”. Retype this. Mind the quotes in “2005”. Try without. They are very important when you get a column with an ambiguous name. With even weirder names you may even need backticks (`2005`).
Filter gapminder for year 2007.
Join it with marriage_df (only matching countries).
Plot linear models of the association between the age of the first marriage in women and gdpPercap, pop, and lifeExp (each in a separate plot).
Plot the same three models but this time map continent on color.
Write a short interpretation of the models.